OpenRaiser/Intern-Atlas huggingface dataset:
Monitoring the Seismic Behavior of a Scaled RC Frame with Intermediate Ductility in a Shaking Table Test
Venue: arXiv cs.CE 2024
Year: 2024

One of the commonly used seismic force-resisting systems in structures is Reinforced Concrete (RC) Intermediate Moment Frames (IMF). Although using the IMF is not allowed in high seismic hazard zones according to ASCE 7-10, it is permitted in both Iran's 2800 Seismic Standard and New Zealand's Seismic Code. This study investigates the seismic behavior of a reinforced concrete IMF subjected to earthquake excitations using shaking table tests on a 2D RC structural model which is designed under the regulations of ACI318-19. The scale factor of 1/2.78 is selected for the frame fabrication due to the size limit of the shaking table. The constructed model has three stories with a height as 115 cm for each story, the clear length of beams as 151 cm, and cross-sectional dimensions of columns and beams as 11 $\times$ 11 cm and 12 $\times$ 11 cm, respectively. The whole structure is supported by a foundation that is 173 cm long, 52 cm wide, and 22 cm deep. Columns and beams are reinforced with 8 mm diameter longitudinal ribbed bars and stirrups with 6 mm diameter. The tests are conducted in stages with increasing peak ground acceleration (PGA) till the failure of the frame. Sarpol-E-Zahab earthquake seismic record is adopted for the experiment. The structural responses (e.g., displacements, longitudinal bars' strain, crack propagation, accelerations) are monitored during the test using both conventional sensors and vison-based sensors. As a comparative study, both conventional sensors and computer vision techniques are used to monitor the health state and to analyze the structural dynamics of the scaled RC frame structure.
Measuring Technological Convergence in Encryption Technologies with Proximity Indices: A Text Mining and Bibliometric Analysis using OpenAlex
Venue: arXiv cs.CY 2024
Year: 2024

Identifying technological convergence among emerging technologies in cybersecurity is crucial for advancing science and fostering innovation. Unlike previous studies focusing on the binary relationship between a paper and the concept it attributes to technology, our approach utilizes attribution scores to enhance the relationships between research papers, combining keywords, citation rates, and collaboration status with specific technological concepts. The proposed method integrates text mining and bibliometric analyses to formulate and predict technological proximity indices for encryption technologies using the "OpenAlex" catalog. Our case study findings highlight a significant convergence between blockchain and public-key cryptography, evidenced by the increasing proximity indices. These results offer valuable strategic insights for those contemplating investments in these domains.
Machine Learning vs Deep Learning: The Generalization Problem
Venue: arXiv cs.LG 2024
Year: 2024

The capacity to generalize beyond the range of training data is a pivotal challenge, often synonymous with a model's utility and robustness. This study investigates the comparative abilities of traditional machine learning (ML) models and deep learning (DL) algorithms in terms of extrapolation -- a more challenging aspect of generalization because it requires the model to make inferences about data points that lie outside the domain it has been trained on. We present an empirical analysis where both ML and DL models are trained on an exponentially growing function and then tested on values outside the training domain. The choice of this function allows us to distinctly showcase the divergence in performance when models are required to predict beyond the scope of their training data. Our findings suggest that deep learning models possess inherent capabilities to generalize beyond the training scope, an essential feature for real-world applications where data is often incomplete or extends beyond the observed range. This paper argues for a nuanced understanding of the structural differences between ML and DL models, with an emphasis on the implications for both theoretical research and practical deployment.
ML4PhySim : Machine Learning for Physical Simulations Challenge (The airfoil design)
Venue: arXiv cs.LG 2024
Year: 2024

The use of machine learning (ML) techniques to solve complex physical problems has been considered recently as a promising approach. However, the evaluation of such learned physical models remains an important issue for industrial use. The aim of this competition is to encourage the development of new ML techniques to solve physical problems using a unified evaluation framework proposed recently, called Learning Industrial Physical Simulations (LIPS). We propose learning a task representing a well-known physical use case: the airfoil design simulation, using a dataset called AirfRANS. The global score calculated for each submitted solution is based on three main categories of criteria covering different aspects, namely: ML-related, Out-Of-Distribution, and physical compliance criteria. To the best of our knowledge, this is the first competition addressing the use of ML-based surrogate approaches to improve the trade-off computational cost/accuracy of physical simulation.The competition is hosted by the Codabench platform with online training and evaluation of all submitted solutions.
Acceleration of digital memcomputing by jumps
Venue: arXiv cs.ET 2024
Year: 2024

In this article, we present the potential benefits of incorporating jumps into the dynamics of digital memcomputing machines (DMMs), which have been developed to address complex optimization problems. We illustrate the potential speed improvement of a DMM solver with jumps over an unmodified DMM solver by solving Boolean satisfiability (SAT) problems of different complicatedness. Our findings suggest that jumps can modify scaling exponents and improve solving times by up to 75 %. Interestingly, the advantages of jumps can be seen in cases where the size of the jump is so large that otherwise the continuous dynamics of voltage variables becomes almost binary.
Towards Memory-Efficient Traffic Policing in Time-Sensitive Networking
Venue: arXiv cs.NI 2024
Year: 2024

Time-Sensitive Networking (TSN) is an emerging real-time Ethernet technology that provides deterministic communication for time-critical traffic. At its core, TSN relies on Time-Aware Shaper (TAS) for pre-allocating frames in specific time intervals and Per-Stream Filtering and Policing (PSFP) for mitigating the fatal disturbance of unavoidable frame drift. However, as first identified in this work, PSFP incurs heavy memory consumption during policing, hindering normal switching functionalities. This work proposes a lightweight policing design called FooDog, which could facilitate sub-microsecond jitter with ultra-low memory consumption. FooDog employs a period-wise and stream-wise structure to realize the memory-efficient PSFP without loss of determinism. Results using commercial FPGAs in typical aerospace scenarios show that FooDog could keep end-to-end time-sensitive traffic jitter <150 nanoseconds in the presence of abnormal traffic, comparable to typical TSN performance without anomalies. Meanwhile, it consumes merely hundreds of kilobits of memory, reducing >90% of on-chip memory overheads than unoptimized PSFP design.
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer
Venue: arXiv cs.CV 2024
Year: 2024

As mobile computing technology rapidly evolves, deploying efficient object detection algorithms on mobile devices emerges as a pivotal research area in computer vision. This study zeroes in on optimizing the YOLOv7 algorithm to boost its operational efficiency and speed on mobile platforms while ensuring high accuracy. Leveraging a synergy of advanced techniques such as Group Convolution, ShuffleNetV2, and Vision Transformer, this research has effectively minimized the model's parameter count and memory usage, streamlined the network architecture, and fortified the real-time object detection proficiency on resource-constrained devices. The experimental outcomes reveal that the refined YOLO model demonstrates exceptional performance, markedly enhancing processing velocity while sustaining superior detection accuracy.
How Multimodal Integration Boost the Performance of LLM for Optimization: Case Study on Capacitated Vehicle Routing Problems
Venue: arXiv cs.AI 2024
Year: 2024

Recently, large language models (LLMs) have notably positioned them as capable tools for addressing complex optimization challenges. Despite this recognition, a predominant limitation of existing LLM-based optimization methods is their struggle to capture the relationships among decision variables when relying exclusively on numerical text prompts, especially in high-dimensional problems. Keeping this in mind, we first propose to enhance the optimization performance using multimodal LLM capable of processing both textual and visual prompts for deeper insights of the processed optimization problem. This integration allows for a more comprehensive understanding of optimization problems, akin to human cognitive processes. We have developed a multimodal LLM-based optimization framework that simulates human problem-solving workflows, thereby offering a more nuanced and effective analysis. The efficacy of this method is evaluated through extensive empirical studies focused on a well-known combinatorial optimization problem, i.e., capacitated vehicle routing problem. The results are compared against those obtained from the LLM-based optimization algorithms that rely solely on textual prompts, demonstrating the significant advantages of our multimodal approach.
NusaBERT: Teaching IndoBERT to be Multilingual and Multicultural
Venue: arXiv cs.CL 2024
Year: 2024

Indonesia's linguistic landscape is remarkably diverse, encompassing over 700 languages and dialects, making it one of the world's most linguistically rich nations. This diversity, coupled with the widespread practice of code-switching and the presence of low-resource regional languages, presents unique challenges for modern pre-trained language models. In response to these challenges, we developed NusaBERT, building upon IndoBERT by incorporating vocabulary expansion and leveraging a diverse multilingual corpus that includes regional languages and dialects. Through rigorous evaluation across a range of benchmarks, NusaBERT demonstrates state-of-the-art performance in tasks involving multiple languages of Indonesia, paving the way for future natural language understanding research for under-represented languages.
CET2: Modelling Topic Transitions for Coherent and Engaging Knowledge-Grounded Conversations
Venue: arXiv cs.CL 2024
Year: 2024

Knowledge-grounded dialogue systems aim to generate coherent and engaging responses based on the dialogue contexts and selected external knowledge. Previous knowledge selection methods tend to rely too heavily on the dialogue contexts or over-emphasize the new information in the selected knowledge, resulting in the selection of repetitious or incongruous knowledge and further generating repetitive or incoherent responses, as the generation of the response depends on the chosen knowledge. To address these shortcomings, we introduce a Coherent and Engaging Topic Transition (CET2) framework to model topic transitions for selecting knowledge that is coherent to the context of the conversations while providing adequate knowledge diversity for topic development. Our CET2 framework considers multiple factors for knowledge selection, including valid transition logic from dialogue contexts to the following topics and systematic comparisons between available knowledge candidates. Extensive experiments on two public benchmarks demonstrate the superiority and the better generalization ability of CET2 on knowledge selection. This is due to our well-designed transition features and comparative knowledge selection strategy, which are more transferable to conversations about unseen topics. Analysis of fine-grained knowledge selection accuracy also shows that CET2 can better balance topic entailment (contextual coherence) and development (knowledge diversity) in dialogue than existing approaches.
MaliGNNoma: GNN-Based Malicious Circuit Classifier for Secure Cloud FPGAs
Venue: arXiv cs.CR 2024
Year: 2024

The security of cloud field-programmable gate arrays (FPGAs) faces challenges from untrusted users attempting fault and side-channel attacks through malicious circuit configurations. Fault injection attacks can result in denial of service, disrupting functionality or leaking secret information. This threat is further amplified in multi-tenancy scenarios. Detecting such threats before loading onto the FPGA is crucial, but existing methods face difficulty identifying sophisticated attacks. We present MaliGNNoma, a machine learning-based solution that accurately identifies malicious FPGA configurations. Serving as a netlist scanning mechanism, it can be employed by cloud service providers as an initial security layer within a necessary multi-tiered security system. By leveraging the inherent graph representation of FPGA netlists, MaliGNNoma employs a graph neural network (GNN) to learn distinctive malicious features, surpassing current approaches. To enhance transparency, MaliGNNoma utilizes a parameterized explainer for the GNN, labeling the FPGA configuration and pinpointing the sub-circuit responsible for the malicious classification. Through extensive experimentation on the ZCU102 board with a Xilinx UltraScale+ FPGA, we validate the effectiveness of MaliGNNoma in detecting malicious configurations, including sophisticated attacks, such as those based on benign modules, like cryptography accelerators. MaliGNNoma achieves a classification accuracy and precision of 98.24% and 97.88%, respectively, surpassing state-of-the-art. We compare MaliGNNoma with five state-of-the-art scanning methods, revealing that not all attack vectors detected by MaliGNNoma are recognized by existing solutions, further emphasizing its effectiveness. Additionally, we make MaliGNNoma and its associated dataset publicly available.
Penetration Testing of 5G Core Network Web Technologies
Venue: arXiv cs.CR 2024
Year: 2024

Thanks to technologies such as virtual network function the Fifth Generation (5G) of mobile networks dynamically allocate resources to different types of users in an on-demand fashion. Virtualization extends up to the 5G core, where software-defined networks and network slicing implement a customizable environment. These technologies can be controlled via application programming interfaces and web technologies, inheriting hence their security risks and settings. An attacker exploiting vulnerable implementations of the 5G core may gain privileged control of the network assets and disrupt its availability. However, there is currently no security assessment of the web security of the 5G core network. In this paper, we present the first security assessment of the 5G core from a web security perspective. We use the STRIDE threat modeling approach to define a complete list of possible threat vectors and associated attacks. Thanks to a suite of security testing tools, we cover all of these threats and test the security of the 5G core. In particular, we test the three most relevant open-source 5G core implementations, i.e., Open5GS, Free5Gc, and OpenAirInterface. Our analysis shows that all these cores are vulnerable to at least two of our identified attack vectors, demanding increased security measures in the development of future 5G core networks.
I DPID It My Way! A Covert Timing Channel in Software-Defined Networks
Venue: arXiv cs.CR 2024
Year: 2024

Software-defined networking is considered a promising new paradigm, enabling more reliable and formally verifiable communication networks. However, this paper shows that the separation of the control plane from the data plane, which lies at the heart of Software-Defined Networks (SDNs), can be exploited for covert channels based on SDN Teleportation, even when the data planes are physically disconnected. This paper describes the theoretical model and design of our covert timing channel based on SDN Teleportation. We implement our covert channel using a popular SDN switch, Open vSwitch, and a popular SDN controller, ONOS. Our evaluation of the prototype shows that even under load at the controller, throughput rates of 20 bits per second are possible, with a communication accuracy of approximately 90\%. We also discuss techniques to increase the throughput further.
On $3$-dimensional MRD codes of type $\langle x^{q^t},x+δx^{q^{2t}},G(x) \rangle$
Venue: arXiv cs.IT 2024
Year: 2024

In this work we present results on the classification of $\mathbb{F}_{q^n}$-linear MRD codes of dimension three. In particular, using connections with certain algebraic varieties over finite fields, we provide non-existence results for MRD codes $\mathcal{C}=\langle x^{q^t}, F(x), G(x) \rangle \subseteq \mathcal{L}_{n,q}$ of exceptional type, i.e. such that $\mathcal{C}$ is MRD over infinite many extensions of the field $\mathbb{F}_{q^n}$. These results partially address a conjecture of Bartoli, Zini and Zullo in 2023.
Revisiting Learning-based Video Motion Magnification for Real-time Processing
Venue: arXiv cs.CV 2024
Year: 2024

Video motion magnification is a technique to capture and amplify subtle motion in a video that is invisible to the naked eye. The deep learning-based prior work successfully demonstrates the modelling of the motion magnification problem with outstanding quality compared to conventional signal processing-based ones. However, it still lags behind real-time performance, which prevents it from being extended to various online applications. In this paper, we investigate an efficient deep learning-based motion magnification model that runs in real time for full-HD resolution videos. Due to the specified network design of the prior art, i.e. inhomogeneous architecture, the direct application of existing neural architecture search methods is complicated. Instead of automatic search, we carefully investigate the architecture module by module for its role and importance in the motion magnification task. Two key findings are 1) Reducing the spatial resolution of the latent motion representation in the decoder provides a good trade-off between computational efficiency and task quality, and 2) surprisingly, only a single linear layer and a single branch in the encoder are sufficient for the motion magnification task. Based on these findings, we introduce a real-time deep learning-based motion magnification model with4.2X fewer FLOPs and is 2.7X faster than the prior art while maintaining comparable quality.
On the Challenges of Transforming UVL to IVML
Venue: arXiv cs.SE 2024
Year: 2024

Software product line techniques encourage the reuse and adaptation of software components for creating customized products or software systems. These different product variants have commonalities and differences, which are managed by variability modeling. Over the past three decades, both academia and industry have developed numerous variability modeling methods, each with its own advantages and disadvantages. Many of these methods have demonstrated their utility within specific domains or applications. However, comprehending the capabilities and differences among these approaches to pinpoint the most suitable one for a particular use case remains challenging. Thus, new modeling techniques and tailored tools for handling variability are frequently created. Transitioning between variability models through transformations from different approaches can help in understanding the benefits and drawbacks of different modeling approaches. However, implementing such transformations presents challenges, such as semantic preservation and avoiding information loss. TRAVART is a tool that helps with transitioning between different approaches by enabling the transformation of variability models into other variability models of different types. This paper discusses the challenges for such transformations between UVL and IVML. It also presents a one-way transformation from the UVL to IVML with as little information loss as possible.
Hybrid Active-Passive RIS Transmitter Enabled Energy-Efficient Multi-User Communications
Venue: arXiv cs.IT 2024
Year: 2024

A novel hybrid active-passive reconfigurable intelligent surface (RIS) transmitter enabled downlink multi-user communication system is investigated. Specifically, RISs are exploited to serve as transmitter antennas, where each element can flexibly switch between active and passive modes to deliver information to multiple users. The system energy efficiency (EE) maximization problem is formulated by jointly optimizing the RIS element scheduling and beamforming coefficients, as well as the power allocation coefficients, subject to the user's individual rate requirement and the maximum RIS amplification power constraint. Using the Dinkelbach relaxation, the original mixed-integer nonlinear programming problem is transformed into a nonfractional optimization problem with a two-layer structure, which is solved by the alternating optimization approach. In particular, an exhaustive search method is proposed to determine the optimal operating mode for each RIS element. Then, the RIS beamforming and power allocation coefficients are properly designed in an alternating manner. To overcome the potentially high complexity caused by exhaustive searching, we further develop a joint RIS element mode and beamforming optimization scheme by exploiting the Big-M formulation technique. Numerical results validate that: 1) The proposed hybrid RIS scheme yields higher EE than the baseline multi-antenna schemes employing fully active/passive RIS or conventional radio frequency chains; 2) Both proposed algorithms are effective in improving the system performance, especially the latter can achieve precise design of RIS elements with low complexity; and 3) For a fixed-size hybrid RIS, maximum EE can be reaped by setting only a minority of elements to operate in the active mode.
OCEL (Object-Centric Event Log) 2.0 Specification
Venue: arXiv cs.DB 2024
Year: 2024

Object-Centric Event Logs (OCELs) form the basis for Object-Centric Process Mining (OCPM). OCEL 1.0 was first released in 2020 and triggered the development of a range of OCPM techniques. OCEL 2.0 forms the new, more expressive standard, allowing for more extensive process analyses while remaining in an easily exchangeable format. In contrast to the first OCEL standard, it can depict changes in objects, provide information on object relationships, and qualify these relationships to other objects or specific events. Compared to XES, it is more expressive, less complicated, and better readable. OCEL 2.0 offers three exchange formats: a relational database (SQLite), XML, and JSON format. This OCEL 2.0 specification document provides an introduction to the standard, its metamodel, and its exchange formats, aimed at practitioners and researchers alike.